mirror of https://github.com/corca-ai/EVAL
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153 lines
6.4 KiB
Python
153 lines
6.4 KiB
Python
import os
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import random
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import torch
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import uuid
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import numpy as np
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from langchain.output_parsers.base import BaseOutputParser
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IMAGE_PROMPT = """
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{i}th file: provide a figure named {filename}. The description is: {description}.
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Please understand and answer the image based on this information. The image understanding is complete, so don't try to understand the image again.
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"""
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AUDIO_PROMPT = """
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{i}th file: provide a audio named {filename}. The description is: {description}.
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Please understand and answer the audio based on this information. The audio understanding is complete, so don't try to understand the audio again.
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"""
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VIDEO_PROMPT = """
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{i}th file: provide a video named {filename}. The description is: {description}.
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Please understand and answer the video based on this information. The video understanding is complete, so don't try to understand the video again.
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"""
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DATAFRAME_PROMPT = """
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{i}th file: provide a dataframe named {filename}. The description is: {description}.
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You are able to use the dataframe to answer the question.
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You have to act like an data analyst who can do an effective analysis through dataframe.
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"""
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AWESOMEGPT_PREFIX = """Awesome GPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.
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Awesome GPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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Awesome GPT is able to process and understand large amounts of various types of files. As a language model, Awesome GPT can not directly read various types of files, but it has a list of tools to finish different visual tasks.
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Each image will have a file name formed as "image/xxx.png"
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Each audio will have a file name formed as "audio/xxx.mp3"
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Each video will have a file name formed as "video/xxx.mp4"
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Each dataframe will have a file name formed as "dataframe/xxx.csv"
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Awesome GPT can invoke different tools to indirectly understand files. When talking about files, Awesome GPT is very strict to the file name and will never fabricate nonexistent files.
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When using tools to generate new files, Awesome GPT is also known that the file may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real file.
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Awesome GPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the file content and file name. It will remember to provide the file name from the last tool observation, if a new file is generated.
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Human may provide new figures to Awesome GPT with a description. The description helps Awesome GPT to understand this file, but Awesome GPT should use tools to finish following tasks, rather than directly imagine from the description.
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Overall, Awesome GPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics."""
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AWESOMEGPT_SUFFIX = """TOOLS
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------
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Awesome GPT can ask the user to use tools to look up information that may be helpful in answering the users original question.
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You are very strict to the filename correctness and will never fake a file name if it does not exist.
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You will remember to provide the file name loyally if it's provided in the last tool observation.
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The tools the human can use are:
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{{tools}}
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{format_instructions}
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USER'S INPUT
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--------------------
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Here is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):
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{{{{input}}}}"""
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ERROR_PROMPT = "An error has occurred for the following text: \n{promptedQuery} Please explain this error.\n {e}"
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os.makedirs("image", exist_ok=True)
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os.makedirs("audio", exist_ok=True)
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os.makedirs("video", exist_ok=True)
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os.makedirs("dataframe", exist_ok=True)
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def seed_everything(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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return seed
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def prompts(name, description):
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def decorator(func):
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func.name = name
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func.description = description
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return func
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return decorator
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def cut_dialogue_history(history_memory, keep_last_n_words=500):
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tokens = history_memory.split()
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n_tokens = len(tokens)
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print(f"hitory_memory:{history_memory}, n_tokens: {n_tokens}")
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if n_tokens < keep_last_n_words:
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return history_memory
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else:
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paragraphs = history_memory.split("\n")
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last_n_tokens = n_tokens
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while last_n_tokens >= keep_last_n_words:
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last_n_tokens = last_n_tokens - len(paragraphs[0].split(" "))
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paragraphs = paragraphs[1:]
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return "\n" + "\n".join(paragraphs)
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def get_new_image_name(org_img_name, func_name="update"):
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head_tail = os.path.split(org_img_name)
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head = head_tail[0]
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tail = head_tail[1]
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name_split = tail.split(".")[0].split("_")
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this_new_uuid = str(uuid.uuid4())[0:4]
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if len(name_split) == 1:
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most_org_file_name = name_split[0]
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recent_prev_file_name = name_split[0]
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new_file_name = "{}_{}_{}_{}.png".format(
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this_new_uuid, func_name, recent_prev_file_name, most_org_file_name
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)
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else:
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assert len(name_split) == 4
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most_org_file_name = name_split[3]
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recent_prev_file_name = name_split[0]
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new_file_name = "{}_{}_{}_{}.png".format(
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this_new_uuid, func_name, recent_prev_file_name, most_org_file_name
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)
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return os.path.join(head, new_file_name)
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def get_new_dataframe_name(org_img_name, func_name="update"):
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head_tail = os.path.split(org_img_name)
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head = head_tail[0]
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tail = head_tail[1]
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name_split = tail.split(".")[0].split("_")
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this_new_uuid = str(uuid.uuid4())[0:4]
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if len(name_split) == 1:
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most_org_file_name = name_split[0]
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recent_prev_file_name = name_split[0]
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new_file_name = "{}_{}_{}_{}.csv".format(
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this_new_uuid, func_name, recent_prev_file_name, most_org_file_name
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)
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else:
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assert len(name_split) == 4
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most_org_file_name = name_split[3]
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recent_prev_file_name = name_split[0]
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new_file_name = "{}_{}_{}_{}.csv".format(
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this_new_uuid, func_name, recent_prev_file_name, most_org_file_name
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)
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return os.path.join(head, new_file_name)
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